Query across connected sources
Connect a database, warehouse, spreadsheet, SaaS tool, or MCP server and ask questions across all of them from one place. Sequel figures out where the data lives and how to retrieve it.
Sequel
RivendicaSequel connects marketing, product, and finance data for AI agents, letting Claude, Cursor, and ChatGPT answer questions in plain English.
What Sequel does
Sequel is a data layer for AI agents that connects marketing, product, and finance data so tools like Claude, Cursor, and ChatGPT can answer questions in plain English. It sits between the agent and your sources, handling authentication, credentials, execution, and joins across systems.
The product is designed for teams that want agent-driven analysis without sharing database passwords or API keys with every tool. Sequel can join data across Postgres, Google Analytics, Stripe, warehouses, spreadsheets, and other connected sources, then return answers, charts, and exported results through a controlled workflow.
Core features
Learns team-specific definitions
Sequel learns your schema, metric definitions, terminology, and team conventions so answers can follow the way your organization already measures performance.
Governed access and execution
A governed data layer sits between your agents and your data, handling auth, encrypted credentials, and controlled execution so agents never need direct access to secrets.
Analysis and visualization outputs
Generate charts and inspect results in sortable, filterable data tables. The docs also note a built-in Python sandbox for computation, charting, and exporting.
Team collaboration
Use shared workspaces and Slack access to collaborate on questions, connections, and query history with teammates.
MCP-based agent workflow
The integrations page and pricing page both indicate support for MCP-capable workflows, so the same connections can be used through supported AI tools and the CLI.
Common use cases
Cross-source analysis
Ask one question that spans multiple systems, such as revenue in Postgres, sessions in Google Analytics, and payments in Stripe, without manually stitching exports together.
Marketing and revenue reporting
Let the product or growth team ask for metrics like CAC, blended ROAS, signups, or top landing pages and get back a governed answer based on the team’s definitions.
Ad hoc analysis and reporting
Use the built-in analysis workflow to generate charts, inspect result sets, and export findings directly from the agent conversation.
Collaborative team workflows
Share access in a team workspace or through Slack so multiple people can ask questions and reuse the same connections and query history.
Multi-agent access
Connect Sequel once and then use the same data layer in supported AI tools and the CLI, instead of reconfiguring credentials for each agent separately.
Pros and Cons
Pros
- Connects to multiple source types, including databases, warehouses, spreadsheets, SaaS tools, and MCP servers.
- Keeps credentials inside Sequel rather than exposing them to agents.
- Supports cross-source questions, including joins across systems such as revenue, analytics, and payments.
- Returns more than plain text by supporting charts, data tables, computation, and exports.
- Includes collaboration features such as shared workspaces and Slack access on relevant plans.
Cons
- The collected sources do not show a full list of supported features for every integration, so some capabilities are easier to confirm than others.
- Several pages point to the docs or integrations list for setup details, which suggests implementation requires checking source-specific guides.
- The pricing page shows an Enterprise option with custom terms, but the collected text does not include the exact security or deployment details beyond self-hosted and SSO/SAML.
FAQ
How does Sequel connect with AI tools?
Sequel connects once to cloud databases, warehouses, SaaS tools, and other MCP-compatible sources. After that, AI tools such as Claude, Cursor, and ChatGPT can ask questions through Sequel without being given the underlying credentials.
What data sources does Sequel support?
The source material shows support for cloud-hosted Postgres and MySQL, warehouses such as BigQuery and ClickHouse, and SaaS tools including Stripe, Google Analytics, HubSpot, and Google Sheets. The integrations page also lists other sources such as PostHog, Mixpanel, Amplitude, Search Console, and Apollo.io.
Is there a free plan?
Yes. The pricing page offers a Free plan, Pro, Team, and Enterprise. The Free plan is limited to one data source and one user, while paid plans add more capacity and collaboration features.
What kinds of outputs can Sequel produce?
Sequel says its built-in Python sandbox can compute, chart, and export results, and the features page also highlights automatic charts and data tables. This suggests it is designed to return both answers and analysis outputs rather than plain text only.
How long does setup take?
The source does not provide a full setup walkthrough in the collected text, but it says you connect your sources once, authorize access through Sequel, and then use the same connections across supported AI tools and the CLI.
Quick Facts
- Category
- AI data analyst / data layer
- Primary users
- Data, analytics, engineering, and operations teams
- Supported tools
- Claude, Cursor, ChatGPT, and other MCP-capable agents
- Data sources
- Cloud databases, warehouses, spreadsheets, SaaS tools, and MCP servers
- Pricing model
- Free, Pro, Team, and Enterprise plans
- Website
- sequel.sh
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